ConceptQuantum Computing

Quantum Annealing

Quantum annealing is a quantum computing metaheuristic that solves optimization and sampling problems by encoding them into the energy landscape of a quantum system and slowly evolving it toward a low-energy (ideally ground) state. It matters because many industrial and scientific problems—from logistics to portfolio optimization—can be framed as combinatorial optimizations where classical methods struggle to find good solutions at scale.

Key Features

  • •Specialized for combinatorial optimization and sampling problems formulated as Ising or QUBO models
  • •Uses quantum tunneling and adiabatic evolution to escape local minima in complex energy landscapes
  • •Analog, noise-tolerant computation model that can be useful even with relatively noisy qubits
  • •Scales to thousands of physical qubits on current commercial hardware (e.g., D-Wave systems) for large problem embeddings
  • •Supports hybrid quantum-classical workflows where classical solvers pre/post-process and guide anneals

Pricing

Unknown

Alternatives

Gate-based Quantum Computing (Circuit Model)Quantum Approximate Optimization Algorithm (QAOA)Simulated AnnealingTabu Search and Other MetaheuristicsMixed-Integer Programming (MIP) Solvers

Links

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Industries Using Quantum Annealing

manufacturing1

Often Used With

Linear Programming (1)Quantum Annealer (1)

Quick Stats

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Total Use Cases
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Industries
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